Probabilistic faster R-CNN with stochastic region proposing: Towards object detection and recognition in remote sensing imagery

Object detection is one of the most important tasks involved in intelligent agriculture systems, especially in pest detection. This paper focuses on a most devastated agricultural disaster: grasshopper plagues. Grasshopper detection and monitoring is of paramount importance in preventing grasshopper plagues. This paper proposes a probabilistic faster R-CNN algorithm with stochastic region proposing, where a probabilistic region proposal network, an image classification network, and an object detection network are integrated to detect and locate grasshoppers. More specifically, in the proposed framework, the probabilistic region proposal network considers attributes (e.g. size, shape) of region proposals and the image classification network identifies the existence of grasshoppers while the object detection network scores recognition confidence for a region proposal. By integrating these three networks, the uncertainty can be passed from end to end, and the final confidence is obtained for each region proposal can be explicitly quantified. To enhance algorithm robustness, a stochastic region proposing algorithm is developed to screen region proposals rather than using a predetermined threshold. The proposed algorithm is validated by recently collected grasshopper datasets. The experimental results demonstrate that the proposed algorithm not only outperforms competing algorithms in terms of average precision (0.91), average missed rate (0.36), and maximum F-1-score (0.9263), but also reduces the false positive rate of recognising the existence of grasshoppers in an open field. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Object detection is crucial in intelligent agriculture, especially with regards to pest detection. This paper proposes a probabilistic faster R-CNN algorithm for grasshopper detection, integrating a probabilistic region proposal network, an image classification network, and an object detection network. Experimental results show that the proposed algorithm outperforms competitors in accuracy and false positive reduction when detecting grasshoppers in open fields.